Assisting decision-makers select multi-dimensionally efficient infrastructure designs – Application to urban drainage systems. (15th June 2023)

• Record Type: Journal Article
• Authors: Seyedashraf, Omid; Bottacin-Busolin, Andrea; Harou, Julien J.
• Is Part Of: Journal of environmental management. Volume 336(2023)
• Publication Date: 2023-06-15
• Subjects: Multi-objective design -- Pareto-front representation -- Sustainable urban drainage systems -- Fuzzy data clustering; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2023.117689
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Multi-objective design approaches can help identify future infrastructure system designs that appropriately balance different engineering, environmental, and other societal goals. Planners benefit from assessing the trade-offs implied by the best-performing infrastructure system solutions. However, a large number of possible efficient system designs, obtained when using multi-objective optimization, can be overwhelming to interpret. This study attempts to aid decision-making in multi-criteria infrastructure system design by reducing the complexity of the identified set of efficient infrastructure designs, i.e., the Pareto-front. A soft clustering algorithm is applied, which identifies similarities between solutions, partitions the front accordingly, and selects a set of representative solutions while preserving the multi-dimensional structure of the solutions on the efficiency frontier. Three post-optimization decision-making metrics are introduced to help quantify the overall performance of the Pareto-optimal designs to further summarize design process outputs for decision-makers. We apply the method to an illustrious urban drainage network case study. Results show how the approach can simplify Pareto-fronts with thousands of solutions into sets of highlighted designs that aid interpreting the trade-offs implied by the best-performing simulated systems. Highlights: A new multi-criteria decision-making framework is proposed for the design of SuDS. The framework uses multi-objective evolutionary optimization and data clustering. The number of Pareto-optimal designs is efficiently narrowed down. The distributional structure of the Pareto-front is preserved.